
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))


# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "54"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder


# Use environment variables to figure out path to the csvs and auto_metrics
antenna = os.environ["ANTENNA"]
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')

antenna = "176"
csv_folder = "/home/obs/src/H5C_Notebooks/_rtp_summary_"
auto_metrics_folder = "/home/obs/src/H5C_Notebooks/auto_metrics_inspect"


display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))


import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna


# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, '*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')

Found 104 csvs in /home/obs/src/H5C_Notebooks/_rtp_summary_
Found 100 auto_metrics notebooks in /home/obs/src/H5C_Notebooks/auto_metrics_inspect


# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0

def jd_to_summary_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'

def jd_to_auto_metrics_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'


this_antenna = None
jds = []

# parse information about antennas and nodes
for csv in csvs:
    df = pd.read_csv(csv)
    for n in range(len(df)):
        # Add this day to the antenna
        row = df.loc[n]
        antnum = row['Ant']
        if antnum != int(antenna):
            continue
            
        if this_antenna is None:
            this_antenna = Antenna(row['Ant'], row['Node'])
        jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
        jds.append(jd)
        this_antenna.add_day(jd, row)
        break


# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]

df = pd.DataFrame(to_show)

# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
    df[col] = bar_cols[col]

z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
    df[col] = z_score_cols[col]

ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
    df[col] = ant_metrics_cols[col]

redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]   
for col in redcal_cols:
    df[col] = redcal_cols[col]

# style dataframe
table = df.style.hide_index()\
          .applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
          .background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
          .background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
          .applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
          .format({col: '{:,.4f}'.format for col in z_score_cols}) \
          .format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
          .format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
          .set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])


display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))


html_to_display = ''
for am_html in auto_metric_htmls:
    # read html into a list of lines
    with open(am_html) as f:
        lines = f.readlines()
    
    # find section with this antenna's metric plots and add to html_to_display
    jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
    try:
        section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
    except ValueError:
        continue
    html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
    for line in lines[section_start_line + 1:]:
        html_to_display += line
        if '<hr' in line: 
            break


HTML(html_to_display)

JDs	A Priori Status	Auto Metrics Flags	Dead Fraction in Ant Metrics (Jee)	Dead Fraction in Ant Metrics (Jnn)	Crossed Fraction in Ant Metrics	Flag Fraction Before Redcal	Flagged By Redcal chi^2 Fraction	ee Shape Modified Z-Score	nn Shape Modified Z-Score	ee Power Modified Z-Score	nn Power Modified Z-Score	ee Temporal Variability Modified Z-Score	nn Temporal Variability Modified Z-Score	ee Temporal Discontinuties Modified Z-Score	nn Temporal Discontinuties Modified Z-Score	Average Dead Ant Metric (Jee)	Average Dead Ant Metric (Jnn)	Average Crossed Ant Metric	Median chi^2 Per Antenna (Jee)	Median chi^2 Per Antenna (Jnn)
2459594	digital_ok	0.00%	0.00%	0.00%	0.00%	2.16%	24.86%	0.068749	-0.226158	0.408306	-0.926318	-0.552905	-0.549489	2.248728	2.145603	0.7380	0.7636	0.2775	3.214716	3.478371
2459593	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.588708	-0.761226	0.665345	-0.799438	-0.350216	-0.256338	3.879407	4.813314	0.7051	0.7042	0.2679	5.417760	5.199104
2459592	digital_ok	0.00%	100.00%	100.00%	0.00%	-	-	0.713664	-0.767981	0.866332	-0.808824	-0.976400	-1.095616	-1.972176	-0.166284	0.0309	0.0285	0.0013	nan	nan
2459591	digital_ok	0.00%	0.00%	0.00%	0.00%	1.62%	0.00%	2.528781	-0.884094	0.764023	-0.918108	-1.122225	-0.938127	-0.689748	0.756625	0.6323	0.6546	0.3899	1.462875	1.472591
2459590	digital_ok	0.00%	0.00%	0.00%	0.00%	1.62%	0.00%	2.838081	-0.443012	0.969030	-0.747299	0.207088	-1.276953	0.075755	0.347539	0.6372	0.6533	0.3829	1.267617	1.227024
2459589	digital_ok	0.00%	0.00%	0.00%	0.00%	2.69%	20.43%	1.555789	-0.813309	0.879659	-0.960262	0.082988	0.431913	2.519488	3.244282	0.6610	0.6696	0.3164	1.721560	1.575663
2459588	digital_ok	0.00%	0.00%	0.00%	0.00%	2.69%	0.00%	1.013762	-0.778417	0.667980	-0.579641	-0.609468	-0.400191	3.237371	3.742929	0.7511	0.7425	0.2333	2.211784	2.117671
2459587	digital_ok	0.00%	-	-	-	3.50%	96.50%	2.447981	-0.671820	1.866578	-0.601148	-0.739463	-0.982855	-1.223167	2.338349	nan	nan	nan	76.060757	63.635180
2459586	digital_ok	0.00%	0.00%	0.00%	0.00%	7.03%	0.00%	1.203666	-0.579912	-0.635118	0.019034	-0.742404	-0.832011	-0.487532	1.186349	0.6141	0.6343	0.3870	1.467746	1.489592
2459585	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.499024	0.108162	-0.901883	-0.462838	-0.771120	3.328944	-1.609221	-0.867514	0.0333	0.0322	0.0011	0.000000	0.000000
2459584	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	6.589082	0.016867	0.162130	0.141575	-1.327281	-1.211411	0.086657	0.734803	0.5780	0.5985	0.3788	4.171402	4.657919
2459583	digital_ok	0.00%	0.00%	0.00%	0.00%	3.23%	0.00%	3.559118	-1.122522	-0.625545	-0.400071	0.490930	-0.678737	-0.551067	0.987171	0.6337	0.6547	0.4025	1.340218	1.387957
2459582	digital_ok	0.00%	0.00%	0.00%	0.00%	2.70%	0.00%	2.887341	-0.859818	-0.311158	-0.273051	-0.586230	-1.029507	-0.691297	0.321224	0.6339	0.6535	0.4059	1.365350	1.325046
2459581	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	4.236594	-0.939741	-0.738368	-0.782282	-0.135772	-0.818579	-0.354686	0.817270	0.6360	0.6562	0.4056	3.236243	3.265368
2459580	digital_ok	0.00%	0.00%	0.00%	0.00%	3.76%	0.00%	3.187096	-0.736384	-0.097318	-0.675735	-0.136860	-0.567713	-0.103477	1.796839	0.6386	0.6579	0.3986	1.338886	1.353124
2459579	digital_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.403918	-0.702451	-0.077388	-0.085977	-0.294018	-0.868007	-0.566069	0.813702	0.6245	0.6454	0.4109	0.000000	0.000000
2459578	digital_ok	0.00%	100.00%	100.00%	0.00%	-	-	-0.074048	0.006927	-0.164013	-0.691114	-0.886778	-0.995284	-0.689929	-1.477359	0.0305	0.0282	0.0014	nan	nan
2459577	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	16.22%	3.277153	-1.215692	-0.672010	-0.621386	-0.355726	-0.665464	-0.635990	1.040921	0.6359	0.6575	0.3991	1.443985	1.344734
2459576	digital_ok	0.00%	0.00%	0.00%	0.00%	4.84%	0.54%	2.662726	-0.775949	-0.506191	-0.253038	-0.091783	-0.607593	-0.440579	0.140867	0.6593	0.6790	0.4029	1.362968	1.311817
2459575	digital_ok	0.00%	0.00%	0.00%	0.00%	4.30%	0.54%	1.802952	-0.705168	-0.720515	-0.280685	-0.459252	-0.465709	-0.224736	0.048450	0.7060	0.7203	0.3583	1.601426	1.585532
2459574	digital_ok	0.00%	0.00%	0.00%	0.00%	3.78%	0.00%	2.824386	-0.742076	-0.753631	-0.365876	-0.914540	-0.349708	-0.802365	-0.091493	0.6538	0.6765	0.4010	1.440180	1.381531
2459573	digital_ok	0.00%	0.00%	0.00%	0.00%	3.78%	0.00%	3.504156	-0.582118	-0.394993	-0.476894	-0.353481	-0.656420	-0.375577	1.677321	0.6465	0.6707	0.4085	1.413422	1.389763
2459572	digital_ok	0.00%	0.00%	0.00%	0.00%	3.78%	0.00%	3.189249	-0.743664	-0.577307	-0.339363	-0.574411	-0.726464	-0.289642	2.741482	0.6581	0.6791	0.4013	1.468897	1.483189
2459571	digital_ok	0.00%	100.00%	100.00%	0.00%	-	-	0.116540	0.030454	-0.098177	-0.749469	0.204210	-1.370445	-0.786631	-1.655618	0.0308	0.0279	0.0016	nan	nan
2459570	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	8.199357	6.109568	7.502678	8.309130	5.220522	6.514768	3.732587	4.170816	0.6509	0.6890	0.3889	4.704413	5.320431
2459569	digital_ok	0.00%	0.00%	0.00%	0.00%	4.32%	0.00%	3.057935	-0.629449	-0.623778	-0.473723	-0.020099	0.448538	0.121277	0.173424	0.7152	0.7290	0.2727	1.844696	1.751614
2459566	digital_ok	0.00%	0.00%	0.00%	0.00%	4.32%	0.00%	2.565211	-0.832076	-0.604460	-0.324348	0.059676	-0.673379	0.030470	1.210990	0.6423	0.6649	0.4194	1.536523	1.583411
2459565	digital_ok	0.00%	-	-	-	86.67%	0.00%	-0.056602	-0.056602	-0.073735	-0.073735	0.553413	0.553413	0.061081	0.061081	nan	nan	nan	0.000000	0.000000
2459564	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.088583	-1.484484	-3.746401	-4.679071	7.799133	8.671598	-0.407084	0.387503	0.6453	0.6636	0.4139	4.209230	4.406956
2459563	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.910306	6.800551	-0.269204	23.481596	-0.365956	14.560558	0.772455	-1.173755	0.6560	0.6557	0.4054	3.617242	3.096087
2459562	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459561	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	0.00%	1.659364	-0.934808	-0.488125	-0.042113	0.725263	0.574481	0.243700	2.108691	0.6595	0.6824	0.4051	1.563764	1.567103
2459560	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	0.00%	1.473806	-0.977391	-0.344163	-0.042741	1.050933	0.524429	0.128606	0.307649	0.6523	0.6745	0.4184	1.493763	1.452737
2459559	digital_ok	0.00%	0.00%	0.00%	0.00%	5.38%	0.00%	0.928185	0.851699	0.271118	0.096101	-1.231410	-0.999051	-1.378282	-1.198961	0.6402	0.6642	0.4059	1.540758	1.540639
2459558	digital_ok	0.00%	0.00%	0.00%	0.00%	12.37%	1.61%	3.390374	-0.806046	-0.331790	-0.216152	0.187275	-0.622832	-0.029087	1.836697	0.6488	0.6657	0.4173	1.473042	1.477269
2459557	digital_ok	-	100.00%	100.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0298	0.0274	0.0016	nan	nan
2459556	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	0.54%	2.991514	-0.680655	-0.262791	-0.088258	-0.611639	-0.354991	-0.152754	2.169640	0.6526	0.6708	0.4061	1.440445	1.399985
2459554	digital_ok	-	0.00%	0.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.6576	0.6767	0.4180	nan	nan
2459553	digital_ok	-	0.00%	0.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.6537	0.6742	0.4156	nan	nan
2459552	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	0.00%	3.086442	-0.769957	-0.068704	-1.001069	-0.365992	-0.469664	0.965350	1.451941	0.6640	0.6836	0.4090	1.508818	1.564700
2459551	digital_ok	0.00%	0.00%	0.00%	0.00%	5.38%	0.00%	1.262052	-0.931832	-0.658818	-0.145796	0.869081	0.475758	0.045484	0.735604	0.6543	0.6763	0.4106	1.520511	1.491585
2459550	digital_ok	-	99.18%	99.18%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0486	0.0477	0.0010	nan	nan
2459549	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459542	digital_ok	0.00%	0.00%	0.00%	0.00%	22.32%	0.00%	2.822116	-0.696803	-0.608461	-0.627146	-0.621198	-0.428658	-0.040197	0.944559	0.7096	0.7142	0.3890	1.486623	1.434337
2459541	digital_ok	0.00%	1.79%	1.79%	0.00%	21.88%	0.00%	2.560176	-0.714953	0.155815	-0.961344	-0.007955	-0.565316	1.451080	1.224369	0.6997	0.7024	0.3545	1.480156	1.423668
2459540	digital_ok	0.00%	0.00%	0.00%	0.00%	21.88%	0.00%	2.122782	-1.190861	-0.606062	-0.049597	-0.193101	-0.390200	0.250677	0.783173	0.7132	0.7125	0.3707	1.490989	1.514889
2459536	digital_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.142974	-1.216709	0.391080	-0.733475	-0.336574	-0.511402	0.526393	1.597030	0.6757	0.6903	0.4507	0.000000	0.000000
2459535	digital_ok	0.00%	0.00%	0.00%	0.00%	21.43%	0.00%	1.562445	-1.053759	-0.403207	-0.071016	-0.389328	-0.131984	1.668516	2.070018	0.7558	0.7385	0.3885	1.681531	1.611905
2459534	digital_ok	0.00%	0.00%	0.00%	0.00%	20.98%	0.00%	1.818450	-1.232960	-0.235644	-0.078960	-0.216053	-0.120066	0.760639	1.228037	0.7430	0.7292	0.3826	1.541287	1.543294
2459533	digital_ok	0.00%	0.00%	0.00%	0.00%	4.89%	0.00%	2.484031	-0.658870	-0.496277	-0.692376	-0.312563	-0.272692	-0.306760	0.375086	0.6777	0.6922	0.4221	1.699957	1.544130
2459532	digital_ok	0.00%	0.00%	0.00%	0.00%	5.00%	0.00%	3.390098	-0.138642	-0.213014	-0.199679	-0.617639	0.166178	-0.548436	-0.416671	0.6787	0.6918	0.4213	1.576949	1.547138
2459530	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459527	digital_ok	0.00%	-	-	-	100.00%	0.00%	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459524	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459523	digital_ok	100.00%	-	-	-	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459522	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459513	digital_ok	0.00%	100.00%	100.00%	0.00%	-	-	0.229027	0.005829	-0.443712	-0.777170	-1.190798	-0.801010	-0.472136	-1.086805	0.0308	0.0313	0.0022	nan	nan
2459508	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.638033	-1.125203	-0.700895	-0.904329	0.674913	0.672918	4.253230	5.669747	0.8897	0.9215	0.2312	0.000000	0.000000
2459505	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	4.765053	-0.564969	0.433024	0.028943	2.306967	4.280127	0.583307	0.151111	0.0859	0.0654	0.0063	0.000000	0.000000
2459503	digital_ok	0.00%	0.00%	0.00%	0.00%	89.47%	0.00%	0.462614	-0.750072	0.214525	-0.924219	-0.228481	0.549134	-0.374719	-0.453915	0.8584	0.8461	0.4976	0.000000	0.000000
2459501	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	2.107050	-1.172987	0.016683	-1.037526	1.022095	-0.844799	0.826987	0.414313	0.0891	0.0952	0.0117	0.000000	0.000000
2459500	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	1.339831	-0.919762	-0.813610	-1.320196	0.292976	-0.122645	0.731721	0.672720	0.0996	0.0848	0.0128	0.000000	0.000000
2459499	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	1.455901	-0.999217	-0.867149	-1.229340	0.266978	0.304115	2.499973	5.841581	0.0795	0.0822	0.0059	0.000000	0.000000
2459498	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	0.976096	-1.283402	-0.418179	-1.089754	0.661711	-0.232475	-0.636549	-0.115906	0.1015	0.0900	0.0160	0.000000	0.000000
2459497	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	1.303024	-1.190195	0.006948	-1.120641	1.468893	0.080508	-0.641128	-0.017234	0.1015	0.0973	0.0133	0.000000	0.000000
2459496	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	1.096665	-1.571570	-0.397577	-0.988961	0.754130	0.054172	1.293802	0.616571	0.1259	0.1167	0.0186	0.000000	0.000000
2459495	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	0.898009	-1.381403	-0.531928	-0.926018	0.791886	0.247881	3.009692	1.549522	0.1069	0.1064	0.0167	0.000000	0.000000
2459494	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	1.410575	-1.305605	-0.844151	-0.758219	1.716266	0.680692	-0.273543	-0.251585	0.0965	0.1025	0.0127	0.000000	0.000000
2459492	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	5.088566	-0.827929	0.610209	-1.602846	1.047580	-0.846627	-0.350197	0.699568	0.0848	0.1117	0.0196	0.000000	0.000000
2459491	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	4.809137	-0.837813	0.856251	-1.416920	0.142580	-0.539892	0.049613	0.063666	0.0937	0.1177	0.0147	0.000000	0.000000
2459490	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	6.069532	-0.910362	0.733246	-1.978860	0.507149	-0.391201	0.937765	3.364748	0.7206	0.7892	0.3851	0.000000	0.000000
2459489	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	5.010339	-0.992484	0.810158	-1.580050	1.871332	-0.024435	0.655250	1.649291	0.7426	0.7989	0.3311	0.000000	0.000000
2459488	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	4.786333	-0.822129	0.366492	-1.281772	1.861998	-0.116953	1.060723	1.632577	0.7417	0.8005	0.3648	0.000000	0.000000

Antenna Season Report Notebook¶

Antenna 176 Report

Load relevant info from summary CSVs¶

Table 1: Per-Night RTP Summary Info For This Atenna¶

Antenna 176, Node 12:

Load antenna metric spectra and waterfalls from auto_metrics notebooks.¶

Figure 1: Antenna autocorrelation metric spectra and waterfalls.¶

Antenna 176: 2459628

Antenna 176: 2459627

Antenna 176: 2459626

Antenna 176: 2459625

Antenna 176: 2459624

Antenna 176: 2459623

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Antenna 176: 2459609

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Antenna 176: 2459542

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Antenna 176: 2459540

Antenna 176: 2459536

Load antenna metric spectra and waterfalls from `auto_metrics` notebooks.¶